Seed glucosinolate yield is maximized by higher rates of sulfur nutrition than required for seed yield in condiment mustard (Brassica juncea L.)
Bibliographic record
Abstract
Brassica crops require high amounts of inorganic sulfur (S) for optimum yield, and are characterized by the synthesis of S-rich glucosinolates (GSL). Although it is well established that seed and GSL yield can be increased by S fertilizer, the detailed relationship between S supply as primary source and the harvestable sinks of seed GSL and storage proteins is poorly understood. We tested the hypothesis that Brassica juncea mustard seed acts as a secondary S sink, and so require a higher rate of S to achieve maximum seed GSL compared to rates required to attain maximum seed biomass. Our experimental strategy involved comparing responses to available S for seed biomass, GSL, and protein. This was carried out in a protected environment using sand culture for a high-GSL condiment-type homozygous B. juncea genotype. A low-GSL canola-type was used as a control, in order to establish a base-line of response. Significantly more S was required to achieve maximum seed GSL than was required to achieve maximum seed mass. Total seed protein content was not significantly affected by increased S. The high-GSL line appeared to have an efficient mechanism of S supply to the secondary S sink, given the observed increase in seed S with increased S availability. From a practical point of view, increases in seed GSL with S availability suggests that S fertilizer rates should be optimized for maximum seed GSL yield, rather that optimizing for seed yield, as occurs for most other crops.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".